Digital image processing plays crucial role in improving quality of digital images. An important aspect of the digital image processing is noise removal or noise reduction. Noise in an image may include random variation of brightness or color information and may manifest as graininess in the image. Further, it is observed that scan images (digital scans of documents) are more prone to noise. Noise may be introduced in an image in various ways, depending on how the image is created. Noise removal or reduction therefore becomes an important part of image processing and for improving accuracy of extracting data from an image.
One of the conventional systems provides for removing noise in form of speckle from an input noisy images using Image Despeckling Convolutional Neural Network (ID-CNN). The ID-CNN may include a component-wise division of residual layers to estimate the noise speckle, and removing the speckles in the image. Another conventional system provides for a back propagation CNN which may involve adjusting a proportion coefficient of a convolution operation result output by different convolution units in the last convolution layer synthesized by an output layer in the CNN. The adjusted convolutional neural network may then be used for de-noising the image. Another conventional system utilizes a depth full-convolution coding-decoding network for carrying out image noise reduction, while using a convolution layer for coding main content of the image for noise reduction, and a de-convolution layer for decoding abstract content of the image and recovering detailed content of the image.